Single-cell transcriptomic profiling of the aging mouse brain
Methodios Ximerakis, Scott L. Lipnick, Brendan T. Innes, Sean K. Simmons, Xian Adiconis, Danielle Dionne, Brittany A. Mayweather, Lan Nguyen, Zachary Niziolek, Ceren Ozek, Vincent L. Butty, Ruth Isserlin, Sean M. Buchanan, Stuart S. Levine, Aviv Regev, Gary D. Bader, Joshua Z. Levin, and Lee L. Rubin.
Nature Neuroscience (2019). DOI:10.1038/s41593-019-0491-3
Abstract
The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.
Single-cell RNAseq data
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The single-cell RNAseq data from old (OX) and young (YX) mouse brain can be explored here using scClustViz. This RShiny app provides interactive viewing of metadata, gene expression distributions, and differential expression testing results. This includes differential expression testing between ages for all cell types. An R package version of this app will be available soon.
Cell-cell interactions
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This is figure 7 from the paper, showing the aging-related changes in predicted cell-cell interactions between cell types of the mouse brain. Clicking the figure or here will take you to an RShiny-based web interface for further exploring this data. The R package that powers this app contains an RData list object with both the edge list and node metadata of predicted cell-cell interactions between cell types in the mouse brain, and their changes with aging. The predictions were generated using CCInx. You can download and explore this data on your local machine in R by running:
# Installation (takes time, but only run once):
install.packages("devtools")
devtools::install_github("BaderLab/AgingMouseBrainCCInx")
# View the data:
library(AgingMouseBrainCCInx)
viewAgingMouseBrainCCInx()